Surface tissue tracking

ABSTRACT

Tissue surface tracking of tissue features is disclosed. First surface imaged features are tracked based on the first and second time spaced images at a first wavelength. Second surface imaged features are tracked based on the first and second time spaced tissue surface images at the second wavelength. Tracking metrics are obtained based on the tracking steps. The tracking steps are combined to provide a combined tracking metric. The combined tracking metric is used in a tissue surface navigation application.

FIELD OF THE INVENTION

The technical field generally relates to surface tissue tracking. Inparticular, surface imaged features are utilized in a tracking process.

BACKGROUND OF THE INVENTION

WO2007072356 discloses a positioning system for a patient monitoringsensor or treatment device with imaging means for detecting a texture orpattern on the skin of a patient to identify a sensor location, an imageprocessing unit which is adapted to learn the required location of asensor by storing the local texture or pattern, and means for guiding auser to reposition the sensor or the user in the desired location byreference to the stored pattern. The texture or pattern may consist of anatural pattern such as a pattern of moles or varying skin color.However, optimal tracking of moles may require different trackingconsiderations than tracking varying color. Further, there are a rangeof other possible biomarkers that could be tracked that may not beoptimally followed by the prior art system.

Thus, it is desired to provide more robust tracking systems and methods.It is further desirable to provide a tracking technique that allows foraccurate and robust tracking of natural tissue surface features that canbe used in many types of navigation applications.

SUMMARY OF THE INVENTION

Hence, there may be a need to provide an improved and facilitated way oftissue feature tracking.

Generally, embodiments of the present invention relate to trackingbiomarkers using more than one tracking process. Each tracking processis tuned to different kinds of biomarkers by using differentlyspectrally filtered images in respective tracking processes. Thetracking results from each tracking process are combined into a singletracking result for use in surface tissue based navigation applications.

The object of the present invention is solved by the subject-matter ofthe independent claims; wherein further embodiments are incorporated inthe dependent claims.

In one aspect of the present disclosure, there is provided a tissuesurface tracking system. The system comprises a data receiving modulefor receiving first and second time spaced tissue surface images, eachtime spaced tissue surface image includes image data at first and seconddifferent wavelengths. The system comprises first and second trackingmodules. The first tracking module is configured to spatially trackfirst tissue surface imaged features based on the first and second timespaced images at the first wavelength to responsively output at leastone first tracking metric. The second tracking module is configured tospatially track second tissue surface imaged features based on the firstand second time spaced images at the second wavelength and toresponsively output at least one second tracking metric. A combinationmodule is configured to combine the at least one first and the at leastone second tracking metric and to responsively output at least onecombined tracking metric for use in a tissue surface navigationapplication. The first and second wavelengths are tuned to differentkinds of biomarkers. In other words, the first wavelength is more suitedto detecting and tracking a first kind of biomarker than the secondwavelength whereas the second wavelength is more suited to detecting andtracking a second kind of biomarker than the first wavelength, whereinthe first and second kinds of biomarker are different.

By running tracking modules that operate on different wavelength imagesin parallel, it is possible to focus on different surface imagedfeatures to provide for a more robust tracking system. For example, skincondition variation can make it difficult for one tracking module andsingle imaging band to successfully and accurately track tissuefeatures. Different wavelength images can be more suited to differentsurface features. The present application addresses this problem byrunning tracking modules in parallel and combining output trackingmetrics to provide for a more reliable system. In this way, the sourceof data operated upon by the tracking modules can be optimized forbiomarker kind.

The tissue surface images may be skin images. The first and secondtissue surface images may be obtained by a multispectral camera. Thetissue surface imaged features may be biomarkers. The time spaced imagesmay comprise reference and subsequent images. The tracking metrics maycomprise spatial displacement information between time spaced images,such as comprising a displacement vector.

In an embodiment, the first tracking module is configured to operate afirst tracking algorithm that is tuned to tracking a first kind ofbiomarker and the second tracking module is configured to operate asecond tracking algorithm that is tuned to tracking a second kind ofbiomarker. Preferably, the first and second tracking algorithms aredifferent.

For example, the tracking modules can operate different image filters,different segmentation approaches, different resolution levels, can betrained to focus on different types of features in order to be optimizedfor allowing identification of specific biomarkers. In a furtherembodiment, the tracking modules are tuned to specific biomarkers andthe imaging wavelength is also optimized for accentuating that kind ofbiomarker.

In an embodiment, the system comprises at least one quality assessmentmodule configured to assess quality of tracking performance for thefirst tracking module and to responsively output at least one firstweighting metric and configured to assess quality of trackingperformance for the second tracking module and to responsively output atleast one second weighting metric. The combination module is configuredto combine the at least one first and the at least one second trackingmetric adaptively based on the at least one first weighting metric andthe at least one second weighting metric. In an embodiment, thecombination module is configured to combine the at least one firsttracking metric and the at least one second tracking metric using aweighting algorithm in which relative weights of the at least one firsttracking metric and the at least one second tracking metric aredetermined based on the at least one first weighting metric and the atleast one second weighting metric. According to such features, thecombination of tracking metrics is adapted depending upon tissueconditions. That is, the performance of certain tracking modules will bedependent upon location and upon the subject. By continually assessingtracking performance, different weights to differently performingtracking modules can be assigned such that the combination of trackingmetrics takes into account relative performance of each module.

In an embodiment, the first tracking module is configured to determinethe at least one first tracking metric using at least one of featurebased tracking and intensity based tracking and the second trackingmodule is configured to determine the at least one second trackingmetric using at least one of feature based tracking and intensity basedtracking.

In an embodiment, the first tracking module and the second trackingmodule are respectively configured to track different kinds ofbiomarkers, wherein a first kind of biomarkers may be superficial skinstructures and a second kind of biomarkers may be subsurface features.For example, the first kind of biomarkers are selected from the group ofmoles, hairs, freckles, pores, spots, melanin pigment, depressions,surface roughness, and the second kind of biomarkers may comprise veinsor arteries.

The system may comprise a camera for capturing the first and second timespaced images at different spectral bands. The use of different spectralbands allows optimal detection of different tissue surface features.

The system of the present disclosure can be used in numerousapplications that require or can make use of surface tissue navigationbased on the combined tracking metric. For example, an image guidedsurgery or medical intervention system can incorporate the presentsystem as can a system for registering intraoperative imaging data,preoperative imaging data or a combination of intraoperative andpreoperative imaging data. The system can be comprised in a skinmonitoring or skin diagnostics system. For example, the skin monitoringsystem may monitor changes in potentially diseased skin features such asmoles identified as being suspicious. A further application would beconsumer electronics products such as a hair removal device, a haircutting device, a hair grooming device and a teeth cleaning device.

In another aspect of the present disclosure, there is provided a methodfor tissue surface tracking. The method comprises receiving first andsecond time spaced tissue surface images, each time spaced tissuesurface image including image data at first and second differentwavelengths. The method comprises tracking first surface imaged featuresbased on the first and second time spaced images at the first wavelengthand responsively outputting at least one first tracking metric. Themethod comprises tracking second surface imaged features based on thefirst and second time spaced tissue surface images at the secondwavelength and responsively outputting at least one second trackingmetric. The method comprises combining the at least one first and the atleast one second tracking metric and responsively outputting at leastone combined tracking metric. The method further comprises using thecombined tracking metric in a tissue surface navigation application suchas any one of the applications described above.

In embodiments, the method is computer implement through at least oneprocessor executing computer readable instructions. The images may beacquired through an imaging device such as a camera. The method maycomprise outputting the combined tracking metric to a system comprisinga computer implemented tissue surface navigation application that usesthe combined tracking metric as part of navigation control.

In an embodiment, the method comprises assessing quality of trackingperformance for the first tracking module and responsively determiningat least one first weighting metric and assessing quality of trackingperformance for the second tracking module and responsively determiningat least one second weighting metric. In a further embodiment, the stepof combining the at least one first tracking metric and the at least onesecond tracking metric comprises using a weighting algorithm in whichrelative weights of the at least one first tracking metric and the atleast one second tracking metric are determined based on the at leastone first weighting metric and the at least one second weighting metric.

In an embodiment, the step of tracking first surface imaged featurescomprises operating a first tracking algorithm optimized with respect toa first kind of surface imaged features and the step of tracking secondsurface imaged features comprises operating a second tracking algorithmoptimized with respect to tracking a second, different, kind of surfaceimaged features.

In yet another aspect of the present disclosure, there is provided acomputer program element adapted to implement a systems and methods asdescribed herein when executed by at least one processor.

In yet another aspect, there is provided a computer readable mediumhaving stored thereon the program element.

These and other aspects of the invention will be apparent from andelucidated with reference to the embodiment(s) described hereinafter.

DESCRIPTION OF THE DRAWINGS

The exemplary embodiments will hereinafter be described in conjunctionwith the following drawing figures, wherein like numerals denote likeelements, and wherein:

FIG. 1 is a schematic functional block diagram of a system for trackingtissue surfaced features according to an exemplary embodiment of thepresent disclosure, wherein the system diagram shows exemplary modulesand data transformations by the system modules;

FIG. 2 is a flowchart illustrating steps of a method for trackingsurface tissue features according to an exemplary embodiment;

FIG. 3 is a flowchart illustrating steps of a tracking algorithmaccording to an exemplary embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following detailed description is merely exemplary in nature and isnot intended to limit the application and uses. Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description.

FIG. 1 is a functional block diagram of a system 20 for tissue surfacetracking according to an exemplary system. FIG. 1 shows processingmodules, the flow of data and transformations in the data performed bythe various processing modules of the tracking system 20.

As used herein, the term module refers to an application specificintegrated circuit (ASIC), an electronic circuit, a processor (shared,dedicated, or group) and memory that executes one or more software orfirmware programs, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality. In particular, themodules described herein include at least one processor, a memory andcomputer program instructions stored on the memory that can be executedby the at least one processor for implementing the various functions andprocesses described herein with respect to the modules and alsodescribed with respect to the flowchart of FIG. 2. Although separatemodules are described herein for particular functions, this does notexclude an integrated topology. Further, the shown modules may bedivided into further sub-modules. The modules are in communication withone another, for example through a data bus, as necessary to implementthe features, processes and systems described herein.

FIG. 1 shows an imaging device 10 for capturing plural time spacedimages 22 at different spectral bands. The imaging device 10 may be anoptical imaging device such as a camera. The imaging device 10 isconfigured to capture sequential images of an area of interest of atissue surface (e.g. skin). The imaging device 10 is multispectralallowing plural images to obtained at different wavelengths (includingdifferent wavelength bands) and to obtain such multispectral images atsuccessive time intervals, e.g. according to a set frame rate of theimaging device 10. The imaging device 10 may include plural filters,each designed to image different tissue surface visible features at aspecific wavelength or wavelengths. The filters may be implemented by aphysical filter or a combination of a physical filter and an imageprocessing filter performed by at least one processor. The imagingdevice 10 may include separate arrays of imaging pixels, e.g. throughseparate cameras, in order to obtain image data 22 at differentwavelengths.

The imaging device 10 may be configured to capture images 22 atrespective wavelengths that are optimized for specific anatomicalfeatures. For example, an infrared wavelength can be used specificallyfor tracking veins and an ultraviolet wavelength can be usedspecifically for tracking moles and freckles. That is, certainwavelengths are able to accentuate specific surface tissue biomarkers.The imaging device can, in embodiments, capture images 22 at wavelengthsthat are optimal for respective biomarkers. Human tissue is partiallytransparent for visual and near-IR wavelengths, allowing surfacefeatures such as melanin pigment and hairs, and subsurface features likeveins or arteries to be identified. Light with wavelengths closer toultraviolet will be optimal for superficial skin features such as molesand freckles.

In one exemplary implementation of the system 20, at least three images22 a, 22 b, 22 c are obtained by the imaging device 10. The imagingdevice 10 may utilize different wavelength filters, such as filters forisolating the images 22 a, 22 b, 22 c at wavelengths of 450 nm, 680 nmand 880 nm, to obtain each of the images 22 a, 22 b, 22 c. Theseexemplary wavelengths are tuned, for instance, to moles or othermelanine pigment features, surface irregularities such as wrinkles, andsubsurface veins, respectively.

In the exemplary system of FIG. 1, a data receiving module 12 is shownfor receiving first and second time spaced tissue surface images 22,each time spaced tissue surface image including image data at first andsecond different wavelengths. The data receiving module 12 is configuredto receive image data 22 a, 22 b, 22 c at different wavelengths λ₁, λ₂,λ₃. Image data 22 can be received at spaced time intervals during whichdisplacement may have occurred in the image content. The presentdisclosure utilizes tracking modules 14 to keep track of the movement asdescribed further in the following.

The data receiving module 12 may comprise an input data interface forreceiving the image data 22. The input data interface may be a networkedcomponent allowing the image data 22 to be received over a wirelessnetwork, such as over the internet or intranet. In the exemplary systemof FIG. 1, the image data 22 is received from the imaging device 10. Thedata receiving module 12 may further comprise a data output interfacefor providing the time spaced image data 22 to respective trackingmodules 14 a, 14 b, 14 c, where each tracking module 14 a, 14 b, 14 creceives imaging data filtered at different wavelengths (includingdifferent wavelength bands) λ₁, λ₂, λ₃. The data receiving module 12 mayinclude a processor and executable computer program instructions fordirecting receipt of the image data 22 and output of the received imagedata 22′ to respective tracking modules 14 a, 14 b, 14 c.

In the exemplary system 20 of FIG. 1, there is provided first, secondand third tracking modules 14 a, 14 b, 14 c. Each tracking module 14 isconfigured to spatially track tissue surface imaged features, e.g.biomarkers, based on time spaced image data 22′ received from the datareceiving module 12. The tracking modules 14 a, 14 b, 14 c arerespectively configured to output a tracking metric {right arrow over(X)}₁, {right arrow over (X)}₂, {right arrow over (X)}₃. The trackingmetric {right arrow over (X)}₁, {right arrow over (X)}₂, {right arrowover (X)}₃ may represent spatial displacement in time spaced images 22′.For example, the tracking metric {right arrow over (X)}₁, {right arrowover (X)}₂, {right arrow over (X)}₃ may comprise a spatial displacementvector defining displacement in time spaced images in three dimensions,which may include a rotational component and/or a linear displacement intwo or three-dimensional Cartesian space. The tracking modules 14 mayinclude an input data interface for receiving image data 22′ from thedata receiving module 12 and an output data interface for outputting atleast one tracking metric {right arrow over (X)}₁, {right arrow over(X)}₂, {right arrow over (X)}₃ and optionally a tracking quality orperformance metric Q₁, Q₂, Q₃, as will be discussed further below. Thetracking modules 14 may further comprise at least one processor andcomputer readable instructions executable by the at least one processorto implement the tissue surface tracking algorithms described herein.Further, the processor and computer readable instructions operate todetermine at least one quality metric Q_(n) for each tracking module 14a, 14 b, 14 c.

Each tracking module 14 a, 14 b, 14 c is configured to operate adifferent tracking algorithm. An exemplary tracking algorithm will bedescribed below with reference to FIG. 3. Each tracking algorithm istuned to track different kinds of biomarker. Exemplary biomarkers thatcan be imaged by tissue surface imaging, include moles, hairs, freckles,spots, melanin pigment, depressions, surface roughness and veins. Forexample, a tracking algorithm that operates on surface roughness wouldfind the best correlation between displaced image intensities of eachimage pair. Typically image patches of each image are normalized and theintensity differences between these normalized patches are used as amatching error. A patch pair with the lowest error is considered thebest displacement candidate. An exemplary tracking algorithm thatoperates on veins, instead would transform the images first to featurevectors (for example by applying the scale-invariant feature transformSIFT algorithm) and calculate descriptors for each of these vectors. Thedisplacement between matching descriptors is then used to calculate thedisplacement between image pairs. Accordingly, one tracking module 14 a,14 b, 14 c may use intensity based tracking tuned to a specificbiomarker and another tracking module 14 a, 14 b, 14 c may use featurebased tracking tuned to a different biomarker. Further, the trackingmodules 14 a, 14 b, 14 c may utilize different reference images(reference descriptors) in feature based tracking that correspond to thespecific biomarker being sought. As has been explained in the foregoing,the image data 22′ may also be spectrally selected so as to maximize anability to identify in the image data 22′ specific biomarkers. As such,not only are the tracking algorithms of the tracking modules 14biomarker tuned, but the image data 22′ itself received by each trackingmodule 14 is filtered to optimize identification of its biomarker. Byrunning multiple spatial trackers 14 in parallel for image data 22′ atdifferent wavelengths, a surface tissue location metric {right arrowover (X)}₁, {right arrow over (X)}₂, {right arrow over (X)}₃ can beobtained that is robust to local anatomical differences in surfacetissue (e.g. presence and amount of hair, spots, and veins).

Referring to FIG. 3, an exemplary tracking algorithm 50 for each module14 a, 14 b, 14 c will be discussed at a general level of detail. Tissuesurface tracking algorithms per se are known to the skilled person andthe present description is representative of one exemplaryimplementation. The tracking algorithm 50 includes receiving steps 30,32 for receiving time spaced reference image data and subsequent imagedata. The reference and subsequent image data are taken from the timespaced image data 22′ described in the foregoing. In step 36, thereference and subsequent image data is compared to register or matchpatterns of biomarkers in the reference image data and the subsequentimage data. The comparison step 36 may make use of feature basedtracking or intensity based tracking. In feature based tracking,patterns of surface tissue features in the subsequent and referenceimage data are identified and matched or registered in the comparisonstep 36. In intensity based tracking, image patches are defined in thereference and subsequent image data, which are compared in thecomparison step 36, thereby allowing intensity based patterns ofbiomarkers in the reference and subsequent image date to be matched orregistered. In both intensity based and feature based methods, asubsequent step 40 can be implemented by which a relative position ordisplacement metric, as one example of the aforementioned trackingmetric, is determined based on the comparison step 36. In particular,displacement in feature or intensity patterns between the reference andsubsequent image data allows a tracking metric to be determined in step38 such as the displacement vectors {right arrow over (X)}₁, {rightarrow over (X)}₂, {right arrow over (X)}₃ described above. The comparedimage data may be chromatic or monochrome. In step 40, the trackingalgorithm, implemented by a tracking module 14, is output for subsequentprocessing as described in the following.

Referring back to FIG. 1, each tracker module 14 a, 14 b, 14 c derives adisplacement metric {right arrow over (X)}_(n) between previous(reference) and subsequent image data 22′ based on a correspondenceanalysis of the image data 22′ such as that described above with respectto step 36 of FIG. 3.

Continuing to refer to FIG. 1, the exemplary system 20 comprises acombination module 16 configured to combine the tracking metrics {rightarrow over (X)}₁, {right arrow over (X)}₂, {right arrow over (X)}₃ tooutput a combined tracking metric {right arrow over (X)}_(C). Thecombined tracking metric {right arrow over (X)}_(C) is used in a tissuesurface navigation application. Exemplary applications for the combinedtracking metric {right arrow over (X)}_(C) will be describedhereinafter. The combined tracking metric {right arrow over (X)}_(C) maybe determined by an averaging function taking as inputs the trackingmetrics {right arrow over (X)}₁, {right arrow over (X)}₂, {right arrowover (X)}₃. Example averaging functions include mean, median and modefunctions. Accordingly, the combined tracking metric {right arrow over(X)}_(C) may be an average displacement metric or an averagedisplacement vector. The combination module 16 may comprise an inputdata interface for receiving the tracking metrics {right arrow over(X)}_(n) from the tracking modules 14 and optionally for receivingweighting metrics W_(n) from a quality assessment module 18 to bedescribed in greater detail hereinafter. The combination module 16 mayinclude a processor and computer readable instructions executable by theprocessor to implement the function of combining a plurality of inputtracking metrics {right arrow over (X)}_(n) as described. Further, thecombination module 16 may include an output data interface for providingthe combined tracking metric {right arrow over (X)}_(C) to an instrument24 that is configured to incorporate the combined tracking metric {rightarrow over (X)}_(C) as part of a control function based on skin surfacenavigation. Examples of the instrument 24 and control functions thereofwill be described hereinafter.

In accordance with embodiments, the combination module 16 makes use ofan averaging algorithm that is adaptive based on a quality assessment ofeach tracking module 14 a, 14 b, 14 c. That is, a relative weight ofcontribution in the combined tracking metric {right arrow over (X)}_(C)is adapted depending upon a determined quality of performance of eachtracking module 14 a, 14 b, 14 c. In particular, quality metrics Q₁, Q₂,Q₃ from each tracking module 14 a, 14 b, 14 c can be compiled by thebelow described quality assessment module 18 to determine upon weightingmetrics W₁, W₂, W₃ to be applied in the averaging algorithm foraveraging the tracking metrics {right arrow over (X)}₁, {right arrowover (X)}₂, {right arrow over (X)}₃. In this way, an adaptive surfacetissue tracking capability is provided that adapts determination of thecombined tracking metric in accordance with the fact that differenttracking modules (e.g. different tracking algorithms and/or differentimaging wavelengths) will perform at different levels of qualitydepending on subject, body part, etc. As such, a location-independent,robust tracking solution is made possible.

In the exemplary system 20 of FIG. 1, a quality assessment module 18 isincluded. The quality assessment module 18 is configured to assessquality of tracking performance of tracking modules 14 based on thequality metrics Q₁, Q₂, Q₃ received from respective tracking modules 14a, 14 b, 14 c. The quality assessment module 18 is configured to processthe quality metrics and determine upon a weighting factor W₁, W₂, W₃ foreach tracking module 14 a, 14 b, 14 c. The weighting factor may bedetermined based on a combination of more than one quality metric Q₁,Q₂, Q₃ received from respective tracking modules 14 a, 14 b, 14 cindicative of quality of performance of the tracking module. The qualitymetrics Q_(n) may be determined by respective tracking modules 14 a, 14b, 14 c based on a parameter representative of number or amount ofbiomarkers in the time spaced image data 22′, a parameter representativeof number or amount of match or registration between time spaced imagedata 22′, and/or a parameter representative of quality of match, e.g.representative of closeness of match or minimal error in comparing thetime spaced image data 22′.

With reference to the discussion of tracking algorithms provided abovewith respect to FIG. 3, for feature based tracking, like that used forvein tracking, the quality metric Q_(n) can be determined by the numberof features (e.g. veins) that are identified in the time spaced imagedata 22′ and/or the number features matched in the time spaced imagedata 22′, the match error between features identified in the time spacedimage data 22′ (e.g. the successive appearance match) and/or a parameterrepresentative of agreement between matched features in time spacedimage data 22′ (e.g. the displacement agreement between identifiedfeatures). For intensity-based tracking, the quality metric Q_(n) can bea function of the frequency content of defined image patches (e.g. theamount of detail) in the time spaced image data 22′, the fit qualitybetween patches (e.g. intensity difference) in the time spaced imagedata 22′ and/or the agreement or inverse error of multiplecorrespondences between patches in the time spaced image data 22′.

The quality assessment module 18 may include an input data interface forreceiving quality metrics Q_(n) from the tracking modules 14. Thequality assessment module may include a processor and computer readableinstructions executable by the processor for assessing the variousquality metrics Q_(n) and determining, based on the quality metricsQ_(n), weighting factors W_(n) associated with each tracking module 14.The quality assessment module 18 may include an output data interfacefor providing the weighting factors Wn to the combination module 16.

In the exemplary system 20 of FIG. 1, the combination module 16 isconfigured to apply the weighting factors W_(n) in the averagingalgorithm to set a relative contribution of each tracking metric {rightarrow over (X)}₁, {right arrow over (X)}₂, {right arrow over (X)}₃ tothe combined tracking metric {right arrow over (X)}_(C). For example, aweighted mean or weighted median may be executed by the combinationmodule 16 based upon the weighting factors W_(n) so as to provide acombined tracking metric that is adaptive to the quality of biomarkersin image data 22′ with respect to both optimal wavelength and optimaltuning of tracking algorithm.

In the exemplary system 20 of FIG. 1, the combined tracking metric{right arrow over (X)}_(C) is output to an instrument 24. The instrument24 may 8 be an image guided surgery or medical intervention system, asystem for registering intraoperative/or imaging data, a skin monitoringor skin diagnostics system or a consumer electronics product such as ahair removal device, a hair cutting device, a hair grooming device and ateeth cleaning device.

In embodiments, the instrument 24 includes a control module 26. Thecontrol module 26 may alternatively be externally provided. The controlmodule 26 is configured to determine upon at least one control functionof the instrument 24 based on the combined tracking metric {right arrowover (X)}_(C). That is, operation of the instrument 24 may be at leastpartly dependent on surface tissue navigation. Surface tissue navigationcan be implemented using the combined tracking metric {right arrow over(X)}_(C) according to schemes known to the skilled person.

In one example, the instrument 24 is an instrument for registeringpre-operative and intra-operative imaging data such as CT or MRI imagingdata. Alternatively or additionally, the instrument 24 is forregistering successive intraoperative images or successive preoperativeimages such as MRI or CT images. Such an instrument 24 may comprise animaging machine for invasive imaging of a patient. The pre-operative andthe intra-operative image data are obtained simultaneously with imagingdata 22 from the imaging device 10. The imaging device 10 has a knownrelationship with the invasive imaging machine. As such, biomarkers canbe tracked from the imaging data 22 according to the methods and systemsdescribed herein to allow for registration of pre-operative andintraoperative imaging data. Such registration can be implemented in thecontrol module 26 based at least partly on the combined tracking metric{right arrow over (X)}_(C) and a display of registered preoperative andintraoperative images may be rendered.

In another example, the instrument 24 comprises an instrument forguiding a medical device. Accurate guidance may be established withreference to surface tissue biomarkers tracked according to the systemsand methods described herein. The control module 26 may be included inthe medical device guidance instrument and can establish a navigationcontrol function at least partly based on the combined registrationmetric {right arrow over (X)}_(C).

In yet another example, a hair or skin treatment device (e.g. haircutting device) may surface tissue navigate based on tracking biomarkersaccording to the systems and methods described herein. The controlmodule 26 may be included into the hair or skin treatment device toestablish at least one hair or skin treatment control function based atleast partly on the combined tracking metric {right arrow over (X)}_(C).

In a further example, the instrument 24 is an instrument for monitoringover time potentially diseased skin features. For example, suspiciousmoles may be monitored over time, where such moles may be cancerous. Theskin features can be identified and monitored with reference tobiomarkers tracked according to systems and methods described herein.For example, shape, location, size and/or color change can be monitored.The control module 26 may be included into such an instrument formonitoring to establish at least one monitoring function (such as skinfeature identification, skin feature measuring, skin feature changedetermination) at least partly based on the combined tracking metric{right arrow over (X)}_(C).

Other systems and instruments that are controlled based at least partlyon surface tissue navigation in order to perform a patient procedure canmake use of surface tissue tracking systems and methods as describedherein.

A method 60 for tissue surface tracking according to the presentdisclosure is represented by the flowchart of FIG. 2. The method is, inembodiments, computer implemented by way of computer readableinstruction being executed by a processor. The method is implemented, inembodiments, by the system 20 described with respect to FIG. 1.

In step 62, image data 22 is received through the data receiving module12. The image data 22 includes time spaced multispectral data. The imagedata 22 may be obtained by a multispectral camera 10 operating differentfilters so that image data 22 is acquired at different wavelengths orspectral bands. Time spaced image data 22′ at different wavelengths isrespectively provided to different tracking processes.

In step 64, tracking processes are performed through the trackingmodules 14 for tracking surface imaged features, e.g. surface tissuebiomarkers. Respective tracking processes are performed on time spacedimage data 22′ filtered to specific wavelengths. In particular, spatialtracking of biomarkers from a reference image to a subsequent image isperformed based on a correlation analysis of the reference andsubsequent images. The tracking processes are respectively tuned to aspecific biomarker kind and the received image data is also tuned tothat biomarker kind. The tracking processes of step 64 produce trackingmetrics X_(n) for each of the tracking modules 14.

In step 66, a quality assessment process is performed through acombination of the tracking modules 14 and the quality assessment module18 to produce weighting metrics W_(n) for use by the combination module16. The quality assessment process comprises, in embodiments, asub-process of determining at least one quality metric Q_(n) througheach of the tracking modules 14. The at least one quality metric Q_(n)is representative of a quality of tracking performance by the trackingmodules 14. The weighting metrics or factors W_(n) can be determined onthe basis of the quality metrics Q_(n).

In step 68, a quality adaptive combination of the tracking metrics X_(n)obtained in step 64 is performed based on the weighting metrics W_(n)obtained in step 66 to determined a combined tracking metric {rightarrow over (X)}_(C). The quality adaptive combination may comprise aweighted averaging algorithm such as weighted mean or weighted median.Different tracking algorithms and different wavelengths of imaging datawill perform differently depending upon surface tissue conditions. Thesystems and methods described herein are able to prioritize betterperforming tracking processes in determining the combined trackingmetric {right arrow over (X)}_(C). Further, the processes of the method60 of FIG. 3 are iteratively performed to allow for continualoptimization as surface tissue conditions vary.

In step 70, the combined tracking metric {right arrow over (X)}_(C) isused or outputted for use in a patient treatment, therapy or diagnosisapplication, e.g. as a control input of a patient treatment, therapy ordiagnosis system, that operate surface tissue navigation. A number ofexamples of such applications are described above, such as CT or MRIimaging data registration, diseased skin feature monitoring, medicaldevice navigation, hair or skin treatment application, etc.

It can be appreciated that surface tissue can vary considerablydepending on location on a subject and from subject to subject. Forexample, different subjects and different surface locations will havevarying amount of hair, spots, and veins. In the case of skin, theappearance of surface tissue can vary from very smooth (i.e. withoutcolor variations, hair or wrinkles) to very detailed (i.e. with melaninspots, hairs and surface roughness and pores). These variations are notonly body location dependent (e.g. moles/freckles are more visible onthe back, blood vessels on arm), but also dependent on subject, race,age and gender. The present disclosures offers a more robust solution tosuch variability in tissue conditions as it runs parallel trackingmodules operating on images directed to different wavelengths, therebyallowing accentuation of different tissue features for tracking.Further, the tracking modules themselves may be differentlyalgorithmically tuned to optimize tracking of different tissue features.Yet further, the combination of tracking results is adapted dependingupon tracking performance so that output results are smooth irrespectiveof tissue conditions.

In another exemplary embodiment of the present invention, a computerprogram or a computer program element is provided that is characterizedby being adapted to execute the method steps of the method according toone of the preceding embodiments, on an appropriate processing system.

The computer program element might therefore be stored on a computerunit, which might also be part of an embodiment of the presentinvention. This computing unit may be adapted to perform or induce aperforming of the steps of the method described above. Moreover, it maybe adapted to operate the components of the above described apparatus.The computing unit can be adapted to operate automatically and/or toexecute the orders of a user. A computer program may be loaded into aworking memory of a data processor. The data processor may thus beequipped to carry out the method of the invention.

This exemplary embodiment of the invention covers both, a computerprogram that right from the beginning uses the invention and a computerprogram that by means of an up-date turns an existing program into aprogram that uses the invention.

Further on, the computer program element might be able to provide allnecessary steps to fulfil the procedure of an exemplary embodiment ofthe method as described above.

According to a further exemplary embodiment of the present invention, acomputer readable medium, such as a CD-ROM, is presented wherein thecomputer readable medium has a computer program element stored on itwhich computer program element is described by the preceding section.

A computer program may be stored and/or distributed on a suitablemedium, such as an optical storage medium or a solid state mediumsupplied together with or as part of other hardware, but may also bedistributed in other forms, such as via the internet or other wired orwireless telecommunication systems.

However, the computer program may also be presented over a network likethe World Wide Web and can be downloaded into the working memory of adata processor from such a network. According to a further exemplaryembodiment of the present invention, a medium for making a computerprogram element available for downloading is provided, which computerprogram element is arranged to perform a method according to one of thepreviously described embodiments of the invention.

It has to be noted that embodiments of the invention are described withreference to different subject matters. In particular, some embodimentsare described with reference to method type claims whereas otherembodiments are described with reference to the device type claims.However, a person skilled in the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject matter alsoany combination between features relating to different subject mattersis considered to be disclosed with this application. However, allfeatures can be combined providing synergetic effects that are more thanthe simple summation of the features.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or exemplary embodiments are only examples, and arenot intended to limit the scope, applicability, or configuration of thedisclosure in any way. Rather, the foregoing detailed description willprovide those skilled in the art with a convenient road map forimplementing the exemplary embodiment or exemplary embodiments. Itshould be understood that various changes can be made in the functionand arrangement of elements without departing from the scope of thedisclosure as set forth in the appended claims and the legal equivalentsthereof.

In the claims, the word “comprising” does not exclude other elements orsteps, and the indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfil the functions ofseveral items re-cited in the claims. The mere fact that certainmeasures are re-cited in mutually different dependent claims does notindicate that a combination of these measures cannot be used toadvantage. Any reference signs in the claims should not be construed aslimiting the scope.

1. A tissue surface tracking system, the system comprising: a datareceiving module for receiving first and second time spaced tissuesurface images, each time spaced tissue surface image including imagedata at first and second different wavelengths, wherein the first andsecond wavelengths are tuned to different kinds of biomarkers; first andsecond tracking modules, wherein the first tracking module is configuredto spatially track first tissue surface imaged features based on thefirst and second time spaced images at the first wavelength toresponsively output at least one first tracking metric, and wherein thesecond tracking module is configured to spatially track second tissuesurface imaged features based on the first and second time spaced imagesat the second wavelength and to responsively output at least one secondtracking metric; at least one quality assessment module configured toassess quality of tracking performance for the first tracking module andto responsively output at least one first weighting metric andconfigured to assess quality of tracking performance for the secondtracking module and to responsively output at least one second weightingmetric, and a combination module configured to adaptively combine the atleast one first and the at least one second tracking metric based on theat least one first weighting metric and the at least one secondweighting metric and to responsively output at least one combinedtracking metric for use in a tissue surface navigation application. 2.The system of claim 1, wherein the first tracking module is configuredto operate a first tracking algorithm that is tuned to tracking a firstkind of biomarker and the second tracking module is configured tooperate a second tracking algorithm that is tuned to tracking a secondkind of biomarker.
 3. (canceled)
 4. The system of claim 3, wherein thecombination module is configured to combine the at least one firsttracking metric and the at least one second tracking metric using aweighting algorithm in which relative weights of the at least one firsttracking metric and the at least one second tracking metric aredetermined based on the at least one first weighting metric and the atleast one second weighting metric.
 5. The system of claim 1, wherein thefirst tracking module is configured to determine the at least one firsttracking metric using at least one of feature based tracking andintensity based tracking and the second tracking module is configured todetermine the at least one second tracking metric using at least one offeature based tracking and intensity based tracking.
 6. The system ofclaim 5 wherein a first wavelength is tuned towards superficial skinfeatures as the first kind of biomarkers, and the second wavelength istuned towards subsurface features as the second kind of biomarkers. 7.The system of claim 6, wherein the first kind of biomarker comprises atleast one of the group of moles, hairs, freckles, pores, spots, melaninpigment, depressions, surface roughness.
 8. The system of claim 6,wherein the second kind of biomarker comprises veins or arteries.
 9. Thesystem of claim 1, comprising a camera for capturing the first andsecond time spaced images at different spectral bands.
 10. An imageguided surgery or medical intervention system or a system forregistering intraoperative imaging data, preoperative imaging data or acombination of intraoperative and preoperative imaging data comprisingthe system of claim
 1. 11. A skin monitoring or skin diagnostics systemcomprising the system of claim
 10. 12. A consumer electronics productcomprising the system of claim 10, wherein the consumer electronicsproduct is selected from the group of a hair removal device, a haircutting device, a hair grooming device and a teeth cleaning device. 13.A method for tissue surface tracking, the method comprising: receivingfirst and second time spaced tissue surface images, each time spacedtissue surface image including image data at first and second differentwavelengths tuned to different kinds of biomarkers; tracking a firstkind of biomarkers as first surface imaged features based on the firstand second time spaced images at the first wavelength and responsivelyproviding at least one first tracking metric, and tracking a second kindof biomarkers as second surface imaged features based on the first andsecond time spaced tissue surface images at the second wavelength andresponsively providing at least one second tracking metric; determiningat least a first quality metric and a second quality metricrepresentative of a quality of a tracking performance; determining atleast a first weighting metric and a second weighting metric on thebasis of the quality metrics; adaptively combining the at least onefirst and the at least one second tracking metric based on the first andsecond weighting metrics, and responsively providing at least onecombined tracking metric; and using the combined tracking metric in atissue surface navigation application.
 14. A computer program elementadapted to perform the method steps of claim 13 when executed by atleast one processor.
 15. A computer readable medium having storedthereon the program element of claim 14.